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113 results for “behavioral genetics”
Data from: Limits to behavioral evolution: the quantitative genetics of a complex trait under directional selection
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Data from: Male and female contributions to behavioral isolation in darters as a function of genetic distance and color distance
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Data from: Genetic correlations among developmental and contextual behavioral plasticity in Drosophila melanogaster
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Data from: The many dimensions of diet breadth: phytochemical, genetic, behavioral, and physiological perspectives on the interaction between a native herbivore and an exotic host
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Data from: Genetic variation in social environment construction influences the development of aggressive behavior in Drosophila melanogaster
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Data from: Parental genetic effects in a cavefish adaptive behavior explain disparity between nuclear and mitochondrial DNA
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Data from: Genetic structure in a dynamic baboon hybrid zone corroborates behavioral observations in a hybrid population
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Data from: The genetic basis of behavioral isolation between Drosophila mauritiana and D. sechellia
Understanding how species form is a fundamental question in evolutionary biology. Identifying the genetic bases of barriers that prevent gene flow between species provides insight into how speciation occurs. Here I analyze a poorly understood reproductive isolating barrier, prezygotic reproductive isolation. I perform a genetic analysis of prezygotic isolation between two closely related species of Drosophila, D. mauritiana and D. sechellia. I first confirm the existence of strong behavioral isolation between D. mauritiana females and D. sechellia males. Next, I examine the genetic basis of behavioral isolation by i) scanning an existing set of introgression lines for chromosomal regions that have a large effect on isolation; and ii) mapping quantitative trait loci (QTL) that underlie behavioral isolation via backcross analysis. In particular, I map QTL that determine whether a hybrid backcross female and a D. sechellia male will mate. I identify a single significant QTL, on the X chromosome, suggesting that few major-effect loci contribute to behavioral isolation between these species. In further work, I refine the map position of the QTL to a small region of the X chromosome.
Data from: Linking genetic merit to sparse behavioral data: does behavior explain genetic variation for maternal care in Soay sheep?
Wild quantitative genetic studies have focused on a subset of traits (largely morphological and life-history), with others, such as behaviors, receiving much less attention. This is because it is challenging to obtain sufficient data, particularly for behaviors involving interactions between individuals. Here, we explore an indirect approach for pilot investigations of the role of genetic differences in generating variation in parental care. Variation in parental genetic effects for offspring performance is expected to arise from among-parent genetic variation in parental care. Therefore, we used the animal model to predict maternal breeding values for lamb growth and used these predictions to select females for field observation, where maternal and lamb behaviors were recorded. Higher predicted maternal breeding value for lamb growth was associated with greater suckling success, but not with any other measures of suckling behavior. Though our work cannot explicitly estimate the genetic basis of the specific traits involved, it does provide a strategy for hypothesis generation and refinement, that we hope could be used to justify data collection costs needed for confirmatory studies. Here results suggest that behavioral genetic variation is involved in generating maternal genetic effects on lamb growth in Soay sheep. Though important caveats and cautions apply, our approach may extend the ability to initiate more genetic investigations of difficult-to-study behaviours and social interactions in natural populations.
Data from: Developmental and genetic effects on behavioral and life-history traits in a field cricket
A fundamental goal of evolutionary ecology is to identify the sources underlying trait variation on which selection can act. Phenotypic variation will be determined by both genetic and environmental factors, and adaptive phenotypic plasticity is expected when organisms can adjust their phenotypes to match environmental cues. Much recent research interest has focused on the relative importance of environmental and genetic factors on the expression of behavioral traits, in particular, and how they compare with morphological and life-history traits. Little research to date examines the effect of development on the expression of heritable variation in behavioral traits, such as boldness and activity. We tested for genotype, environment, and genotype-by-environment differences in body mass, development time, boldness, and activity, using developmental density treatments combined with a quantitative genetic design in the sand field cricket (Gryllus firmus). Similar to results from previous work, animals reared at high densities were generally smaller and took longer to mature, and body mass and development time were moderately heritable. In contrast, neither boldness nor activity responded to density treatments, and they were not heritable. The only trait that showed significant genotype-by-environment differences was development time. It is possible that adaptive behavioral plasticity is not evident in this species because of the highly variable social environments it naturally experiences. Our results illustrate the importance of validating the assumption that behavioral phenotype reflects genetic patterns and suggest questions about the role of environmental instability in trait variation and heritability.
Data from: Complex economic behavior patterns are constructed from finite, genetically controlled modules of behavior
Complex ethological behaviors could be constructed from finite modules that are reproducible functional units of behavior. Here, we test this idea for foraging and develop methods to dissect rich behavior patterns in mice. We uncover discrete modules of foraging behavior reproducible across different strains and ages, as well as nonmodular behavioral sequences. Modules differ in terms of form, expression frequency, and expression timing and are expressed in a probabilistically determined order. Modules shape economic patterns of feeding, exposure, activity, and perseveration responses. The modular architecture of foraging changes developmentally, and different developmental, genetic, and parental effects are found to shape the expression of specific modules. Dissecting modules from complex patterns is powerful for phenotype analysis. We discover that both parental alleles of the imprinted Prader-Willi syndrome gene Magel2 are functional in mice but regulate different modules. Our study found that complex economic patterns are built from finite, genetically controlled modules.
Data from: Mapping of genetic factors that elicit intermale aggressive behavior on mouse chromosome 15: intruder effects and the complex genetic basis
Despite high estimates of the heritability of aggressiveness, the genetic basis for individual differences in aggression remains unclear. Previously, we showed that the wild-derived mouse strain MSM/Ms (MSM) exhibits highly aggressive behaviors, and identified chromosome 15 (Chr 15) as the location of one of the genetic factors behind this escalated aggression by using a panel of consomic strains of MSM in a C57BL/6J (B6) background. To understand the genetic effect of Chr 15 derived from MSM in detail, this study examined the aggressive behavior of a Chr 15 consomic strain towards different types of opponent. Our results showed that both resident and intruder animals had to have the same MSM Chr 15 genotype in order for attack bites to increase and attack latency to be reduced, whereas there was an intruder effect of MSM Chr 15 on tail rattle behavior. To narrow down the region that contains the genetic loci involved in the aggression-eliciting effects on Chr 15, we established a panel of subconsomic strains of MSM Chr 15. Analysis of these strains suggested the existence of multiple genes that enhance and suppress aggressive behavior on Chr 15, and these loci interact in a complex way. Regression analysis successfully identified four genetic loci on Chr 15 that influence attack latency, and one genetic locus that partially elicits aggressive behaviors was narrowed down to a 4.1-Mbp region (from 68.40 Mb to 72.50 Mb) on Chr 15.
Supplementary material 2 from: Gauthey Z, Tentelier C, Lepais O, Elosegi A, Royer L, Glise S, Labonne J (2017) With our powers combined: integrating behavioral and genetic data to estimate mating success and sexual selection. Rethinking Ecology 2: 1-26. https://doi.org/10.3897/rethinkingecology.2.14956
JAGS code for the model : Data type: Programming code.
Supplementary material 1 from: Gauthey Z, Tentelier C, Lepais O, Elosegi A, Royer L, Glise S, Labonne J (2017) With our powers combined: integrating behavioral and genetic data to estimate mating success and sexual selection. Rethinking Ecology 2: 1-26. https://doi.org/10.3897/rethinkingecology.2.14956
Data and model outputs : Data type: Body size, behavioural and genetic data, and model output.
Supplementary material 2 from: Ney G, Schul J (2019) Epigenetic and genetic variation between two behaviorally isolated species of Neoconocephalus (Orthoptera: Tettigonioidea). Journal of Orthoptera Research 28(1): 11-19. https://doi.org/10.3897/jor.28.28888
: Explanation note: Matrix of MS-AFLP called fragments for all individuals.
Figure 5 from: Ney G, Schul J (2019) Epigenetic and genetic variation between two behaviorally isolated species of Neoconocephalus (Orthoptera: Tettigonioidea). Journal of Orthoptera Research 28(1): 11-19. https://doi.org/10.3897/jor.28.28888
Figure 5 Scatterplot of between-individual Euclidean genetic and epigenetic distance showing significant positive correlation between genetic and epigenetic differentiation. The correlation was tested using a Mantel test and 10,000 permutations of the design matrix to determine significance.
Figure 1 from: Ney G, Schul J (2019) Epigenetic and genetic variation between two behaviorally isolated species of Neoconocephalus (Orthoptera: Tettigonioidea). Journal of Orthoptera Research 28(1): 11-19. https://doi.org/10.3897/jor.28.28888
Figure 1 Species assignment based on call pulse period ratio and center frequency. Labeled boxes indicate the calls classified as N.robustus and N.bivocatus. Individuals that fall outside of species classifications were removed from further epigenetic and genetic analyses (as described in Ney and Schul 2017).
Figure 4 from: Ney G, Schul J (2019) Epigenetic and genetic variation between two behaviorally isolated species of Neoconocephalus (Orthoptera: Tettigonioidea). Journal of Orthoptera Research 28(1): 11-19. https://doi.org/10.3897/jor.28.28888
Figure 4 Consensus shared ancestry population structure for epigenetic and genetic loci. A. and C. Bar plots using MS-AFLP loci to estimate genetic (A) and epigenetic (C) structure among N.robustus and N.bivocatus using the software package STRUCTURE. B. and D. Delta K graphs for K = 1–10 genetic clusters showing moderate support for K = 2 genetic clusters (B) and low support for K = 4 epigenetic clusters (D).
Figure 3 from: Ney G, Schul J (2019) Epigenetic and genetic variation between two behaviorally isolated species of Neoconocephalus (Orthoptera: Tettigonioidea). Journal of Orthoptera Research 28(1): 11-19. https://doi.org/10.3897/jor.28.28888
Figure 3 PCoA of N.robustus and N.bivocatus utilizing genetic (A) and epigenetic (B) data. Plotted are the two most informative principal components calculated for the genetic and epigenetic loci datasets, as derived from the MS-AFLP fragment analysis. A. Genetic Euclidean distance with individuals grouped by species assignment. B. Epigenetic Euclidean distance with individuals grouped by species assignment. Group labels show the centroid of the points for each group. The long axis of the ellipse represents the direction of maximum dispersion and the short axis the direction of minimum dispersion.
Figure 2 from: Ney G, Schul J (2019) Epigenetic and genetic variation between two behaviorally isolated species of Neoconocephalus (Orthoptera: Tettigonioidea). Journal of Orthoptera Research 28(1): 11-19. https://doi.org/10.3897/jor.28.28888
Figure 2 Comparison of genome-wide methylation levels between species. Mann-Whitney U test; p<0.005 (**). Between-species significant variation is in total methylation (internal cytosine methylated and hemimethylated fragments).
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.